Home | Data Overview | Multi-Dimensional Analysis | Conclusion

Major | Gender | Career | Ethnicity | First Generation Students | Dependents | Medical Condition | Medical Care | Analysis

Function

To begin, let’s explore the function we created to run all of this statistical analysis:

mysubsetMDS <- function(x){
  mysubset <- df %>% 
    select(starts_with(x))
  
  meta <- metaMDS(mysubset)
  MDS_df <- data.frame(MDS1=meta$points[,1],MDS2=meta$points[,2]) %>%
    cbind(demo)
  return(MDS_df)
}


In English, this function allows us to run the MDS according to each subset of demographic and each subset of question type that we want. Obviously, in this page, we are exploring number of dependents.

Question Types

Science Identity

Let’s explore the science identity subset of questions first. Running our function we created and plotting it, we are left with this image of the plot:

This is great and all, but let’s run an adonis test to see if there is a significant difference in how different number of dependents goals responded to science identity questions:

## 
## Call:
## adonis(formula = si ~ demo$dependents) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                  Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$dependents   1    0.0212 0.021182 0.66268 0.00213  0.504
## Residuals       310    9.9088 0.031964         0.99787       
## Total           311    9.9299                  1.00000

This shows that the differences in answers are not significant, according to number of dependents.


Career Motivation

Next, let’s look into Carer Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = cm ~ demo$dependents) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                  Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$dependents   1    0.0309 0.030903 0.60624 0.00195  0.502
## Residuals       310   15.8020 0.050974         0.99805       
## Total           311   15.8329                  1.00000

This shows that the differences in answers are not significant, according to number of dependents.


Intrinsic Motivation

Now, Intrinsic Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = im ~ demo$dependents) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                  Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$dependents   1    0.0197 0.019670 0.69353 0.00223  0.453
## Residuals       310    8.7922 0.028362         0.99777       
## Total           311    8.8119                  1.00000

This shows that the differences in answers are not significant, according to number of dependents.

Self-Determination

Now, Self-Determination:

And an Adonis test:

## 
## Call:
## adonis(formula = sd ~ demo$dependents) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                  Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)  
## demo$dependents   1    0.0787 0.078723  3.2374 0.01034  0.051 .
## Residuals       310    7.5381 0.024317         0.98966         
## Total           311    7.6168                  1.00000         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

This shows that the differences in answers are not significant, according to number of dependents.

Self-Efficacy

Now, Self-Efficacy:

And an Adonis test:

## 
## Call:
## adonis(formula = se ~ demo$dependents) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                  Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$dependents   1    0.0110 0.010950 0.39961 0.00129  0.656
## Residuals       310    8.4950 0.027403         0.99871       
## Total           311    8.5059                  1.00000

This shows that the differences in answers are not significant, according to number of dependents.

Grade Motivation

Now, Grade Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = gm ~ demo$dependents) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                  Df SumsOfSqs   MeanSqs F.Model      R2 Pr(>F)
## demo$dependents   1    0.0035 0.0035482 0.15971 0.00051  0.885
## Residuals       310    6.8873 0.0222172         0.99949       
## Total           311    6.8909                   1.00000

This shows that the differences in answers are not significant, according to number of dependents.

Competency in Science

Now, Competency in Science:

And an Adonis test:

## 
## Call:
## adonis(formula = sci_comp ~ demo$dependents) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                  Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$dependents   1     0.046 0.046031  1.3264 0.00426  0.266
## Residuals       310    10.758 0.034703         0.99574       
## Total           311    10.804                  1.00000

This shows that the differences in answers are not significant, according to number of dependents.

Personal Community Orientation

Now, Personal Community Orientation:

And an Adonis test:

## 
## Call:
## adonis(formula = per_comm_orient ~ demo$dependents) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                  Df SumsOfSqs   MeanSqs F.Model      R2 Pr(>F)
## demo$dependents   1   0.00537 0.0053707 0.60781 0.00196  0.391
## Residuals       310   2.73921 0.0088362         0.99804       
## Total           311   2.74458                   1.00000

This shows that the differences in answers are not significant, according to number of dependents.

Science Community Orientation

Now, Competency in Science:

Well crap, something is broke there. Let’s try running a different form of MDS, giving us a similar analysis in a different way. We can interpret this data similarly to how we did before:

And an Adonis test:

## 
## Call:
## adonis(formula = sci_comm_orient ~ demo$dependents) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                  Df SumsOfSqs  MeanSqs F.Model     R2 Pr(>F)
## demo$dependents   1    0.0124 0.012390 0.83851 0.0027  0.344
## Residuals       310    4.5806 0.014776         0.9973       
## Total           311    4.5929                  1.0000

This shows that the differences in answers are not significant, according to major.

Conclusion

Ultimately, what we can understand from all of this is that a student’s number of dependents does not have a significant impact on the the differences in answers in our questions.

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